1. Biostatistics and Epidemiology
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Biostatistics and Epidemiology
Lillian Sung, MD PhD
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2. Disclosure Information
Lillian Sung – No disclosures

3. Outline Principles of Use of Biostatistics in Research
Principles of Epidemiology and Clinical Research Design
Applying Research to Clinical Practice

4. Principles of Use of Biostatistics in Research

5. Principles of Use of Biostatistics in Research
1. Types of variables
Distinguish types of variables (eg, continuous, categorical, ordinal, nominal) (slide 7)
Understand how the type of variable (eg, continuous, categorical, nominal) affects the choice of statistical test (slides 7, 12-16)
2. Distribution of Data
Understand how distribution of data affects the choice of statistical test (slides 8, 12, 13)
Differentiate normal from skewed distribution of data (slide 8)
Understand the appropriate use of the mean, median, and mode (slide 8)
Understand the appropriate use of standard deviation (slide 9)
Understand the appropriate use of standard error (slide 9)
3. Hypothesis testing
Distinguish the null hypothesis from an alternative hypothesis (slides 10, 11)
Interpret the results of hypothesis testing (slides 10, 11, 18)
4. Statistical tests
Understand the appropriate use of the chi-square test versus a t-test (slides 12-16)
Understand the appropriate use of analysis of variance (ANOVA) (slides 12, 13)
Understand the appropriate use of parametric (eg, t-test, ANOVA) versus non-parametric (eg, Mann-Whitney U, Wilcoxon) statistical tests (slides 12, 13)
Interpret the results of chi-square tests (slides 15, 16)
Interpret the results of t-tests (slide 14)
Understand the appropriate use of a paired and non-paired t-test (slides 14, 17)
Determine the appropriate use of a 1- versus 2-tailed test of significance (slides 19, 20)
Interpret a p-value (slides 18-21)
Interpret a p-value when multiple comparisons have been made (slide 21)
Interpret a confidence interval (slide 22)
Identify a type I error (slide 23)
Identify a type II error (slide 23)

6. 5. Measurement of association
Differentiate relative risk reduction from absolute risk reduction (slide 24)
Calculate and interpret a relative risk (slide 25)
Calculate and interpret an odds ratio (slide 25)
Interpret a hazard ratio (slide 25)
Understand the uses and limitations of a correlation coefficient (slide 26)
6. Regression
Identify when to apply regression analysis (eg, linear, logistic) (slide 27)
Interpret a regression analysis (eg, linear, logistic) (slide 27)
Identify when to apply survival analysis (eg, Kaplan-Meier) (slides 28-30)
Interpret a survival analysis (eg, Kaplan-Meier) (slides 28-30)
7. Diagnostic tests
Recognize the importance of an independent "gold standard" in evaluating a diagnostic test (slides 31, 32)
Calculate and interpret sensitivity and specificity (slides 31, 32)
Calculate and interpret positive and negative predictive values (slide 31)
Understand how disease prevalence affects the positive and negative predictive value of a test (slide 32)
Calculate and interpret likelihood ratios (slide 33)
Interpret a receiver operator characteristic curve (slide 34)
Interpret and apply a clinical prediction rule (slide 34)
8. Systematic reviews and meta-analysis
Understand the purpose of a systematic review (slide 35)
Understand the advantages of adding a meta-analysis to a systematic review (slide 35)
Interpret the results of a meta-analysis (slide 35)
Identify the limitations of a systematic review (slide 35)
Identify the limitations of a meta-analysis (slide 35)

7. Types of Variables Type Example Dichotomous Induction death yes/no
Categorical/
Nominal
Leukemia type: AML, ALL, CML, other
Ordinal
CTCAE toxicity grade 1, 2, 3, 4, 5
Continuous
Serum creatinine
Survival
Time to death
Example – GVHD outcome
Nature of outcome variable (dichotomous, categorical, ordinal, survival) drives the choice of statistical tests

8. Distribution of Data Central Tendency
Skewed
Normal
Mode
Median
Mean=Median=Mode
Mean
Mean – average value (use if normal)
Median – middle value (use if skewed)
Mode – most common value
Distribution drives the choice of statistical tests

9. Standard Deviation and Standard Error
Standard deviation – average spread from the mean
Wider the spread, the larger the SD
Standard error – SD/sqrt(n)
Calculate standard deviation by taking each point and figuring out the distance to the mean – adding up all those values and dividing roughly by the # of pairs
Conceptually, SE is a special kind of standard deviations

10. Hypothesis Testing Define null hypothesis (treatment does not work)
Alternate hypothesis – treatment works
Determine probability of observing data as or more extreme assuming the treatment does not work
If this probability is sufficiently small – reject null hypothesis

11. Rejection of Null Hypothesis
Z=+1.96
Z=-1.96
Each test statistic has a distribution
The distribution of the test statistic is divided into an area of failure to reject the null hypothesis and area of rejection of the null hypothesis
Rejection of the null hypothesis

12. Distribution drives the choice of statistical tests
Nature of outcome variable (dichotomous, categorical, ordinal, survival) drives the choice of statistical tests
Distribution drives the choice of statistical tests

13. Outcome is Continuous Mann Whitney U (Wilcoxon rank sum)
Number of Groups of Interest
Parametric
Non-parametric
Two
Student’s t-test
Mann Whitney U (Wilcoxon rank sum)
Three or more
Analysis of variance
(ANOVA)
Kruskal Wallis

14. Student’s T-test Two groups with a continuous outcome measure
t = mean(gp1) – mean(gp2)
Variance
Larger t ~ smaller p value
Assumptions:
Data normally distributed
Observations independent
If data are matched (eg blood pressure before and after)
should use paired T-test

15. Outcome is Dichotomous
Number of Groups of Interest
Two or More
Chi square or Fisher’s exact test

16. Chi Square Test Compares proportions in 2 or more groups:
Calculate expected values for each cell
X2 =∑ (O-E)2/E
Larger X2 ~ smaller p value
WBC ≥ 200
WBC < 200
Induction Death Yes A B
Induction Death No C D

17. Matched/Paired Versus Independent
Are the exposure groups independent of one another?
Ways to induce matching:
Compare outcome within an individual
Eg. Pre-post intervention, cross-over trial
Can create match by how you select subjects
Eg. In case-control study, can match cases and controls

18. P Values
P value: probability of obtaining a test statistic at least as extreme as the one actually observed assuming the null hypothesis is true
Translation: Chance of getting the results you saw assuming that the treatment doesn’t work
P=0.05: 5% chance of seeing data as extreme assuming null hypothesis
Translation: Assuming that the treatment doesn’t work, there is a 5% chance of observing a difference by chance alone

19. Two vs One Sided P Value Two-sided P value
Two-sided P value will evaluate both that the treatment is better and that the treatment is worse than control
Typically use two-sided P values
Z=-1.96
Z=+1.96
2.5%
2.5%
Each test statistic has a distribution
The distribution of the test statistic is divided into an area of failure to reject the null hypothesis and area of rejection of the null hypothesis
Rejection of the null hypothesis
Two-sided P value

20. Two vs One Sided P Value One-sided P value
One-sided P value will only evaluate that the treatment is better than control
Easier to show “statistical significance”
Less commonly used
Z=+1.645 5%
Each test statistic has a distribution
The distribution of the test statistic is divided into an area of failure to reject the null hypothesis and area of rejection of the null hypothesis
Rejection of the null hypothesis
One-sided P value

21. Multiple testing: if you do many tests, increase the chance of finding P < 0.05 just by chance alone, therefore need to adjust P value for multiple comparisons

22. Confidence Intervals
Confidence interval: probability that the interval contains the true parameter
For example, 95% CI around the mean – 95% probability that the interval contains the true mean

23. Type I and Type II Errors
TRUTH
Difference Exists
No Difference Exists
CONCLUSION FROM TEST *
Power
Sensitivity
Type I or alpha error
False positive
Type II or beta error
False negative
Two errors can be made in tests of hypothesis
Type I error – is when we reject the null hypothesis and in truth, no difference exists. This is also referred to as the alpha error.
Type II error – is when we fail to reject the null hypothesis and a difference really exists
Analogies: Type I error is like a false positive rate
Type II error is like a false negative rate
Power is like sensitivity of test

24. Measures of Association
Outcome
Yes No
Group
Treatment
A (1)
B (9)
Control
C (4)
D (6)
Risk of outcome in treatment group = 0.10 (A/A+B)
Risk of outcome in control group = (C/(C+D)
Absolute risk reduction: decrease in risk of an outcome associated with an intervention
ARR = C/(C+D)- A/(A+B) = 0.40 – 0.10 = 0.30
Relative risk reduction: absolute risk reduction divided by event rate in the control arm
RRR = 0.30/0.40=0.75
Number needed to treat = 1/ARR = 1/0.30 = 3.3

25. Odds Ratio and Relative Risk
Outcome
Yes No
Group
Treatment A B
Control C D
Risk in treatment = A/(A+B); Risk in control = C/(C+D)
Relative risk = A/(A+B)
C/(C+D)
RR 2.5 = 2.5 times risk of outcome if treated
Odds in treatment = A/B; Odds in control = C/D
Odds ratio = A/B = AD/BC
C/D
OR 2.5 = 2.5 times odds of outcome if treated
Hazard ratio: analogous to a relative risk used in survival analysis
If predictor and outcome are dichotomous – get the same two by two table

26. Correlation Coefficient
Strength of the linear relationship between two numbers
- 1 ≤ r ≤ +1
r = -1.0
r = 1.0
= r
Strength of the linear relationship between two numbers
Measure of correlation
Not measure of concordance

27. Regression
Used to define relationships or to predict an outcome based on one or more exposure variables
Univariate: single exposure variable, single outcome
Multivariable: multiple exposure variables, single outcome
Type of regression depends on nature of outcome variable
Dichotomous – logistic regression
Continuous – linear regression
Survival – Cox proportional hazards model

28. Survival Analysis Outcome is time to event
Censor patients who don’t have an event when last observed
Most data is right censored
censored
START STUDY
STOP STUDY

29. Kaplan-Meier Method Example of how to display survival data
Calculates survival probability whenever an event occurs
Survival
Months
Use to describe survival at a given time eg survival at 30 months is 40%

30. When to Use Survival Analysis
Time to event data
Each individual - different length of follow-up
Patients may be lost to follow-up
Patients may be censored

31. Diagnostic Tests Gold Standard True False New Test Positive A B
Negative C D
Need gold standard
Sensitivity = A/(A+C) – proportion of those with the disease who have a positive test
Specificity – D/(B+D) – proportion of those without the disease who have a negative test
Positive predictive value = A/(A+B) – proportion of those who test positive who have the disease
Negative predictive value = D/(C+D) – proportion of those who test negative who do not have the disease
Expressed another way – sensitivity = TP/(TP+FN)

32. Influence of Prevalence on Diagnostic Tests
Gold Standard
True
False
New Test
Positive A B
Negative C D
Because (A+C) is prevalence of disease:
Prevalence influences PPV and NPV
Does not influence sensitivity and specificity

33. Likelihood Ratios
LR+: How much the odds of a disease increase when the test is positive
= sensitivity/(1-specificity)
LR-: How much the odds of a disease decrease when the test is negative
= (1-sensitivity)/specificity

34. Other Diagnostic Test Issue
Receiver operator curve: to evaluate the optimal threshold for a diagnostic test
Clinical prediction rule: using signs, symptoms and tests to predict a clinical outcome
Sensitivity
(1-Specificity)

35. Systematic Reviews
Identify studies that address a similar question – and synthesize the data either qualitatively or quantitatively
Quantitative review – meta-analysis
Limitations
Heterogeneity in treatment effect – may not be appropriate to combine
Publication bias

36. Principles of Epidemiology and Clinical Research Design and Applying Research to Clinical Practice

37. B. Principles of Epidemiology and Clinical Research Design
1. Study types
Distinguish between Phase I, II, III, and IV clinical trials (slide 39)
Recognize a retrospective study (slide 40)
Understand the strengths and limitations of retrospective studies (slide 40)
Recognize a case series (slide 41)
Understand the strengths and limitations of case series (slide 41)
Recognize a cross-sectional study (slide 42)
Understand the strengths and limitations of cross-sectional studies (slide 42)
Recognize a case-control study (slide 43)
Understand the strengths and limitations of case-control studies (slide 44)
Recognize a longitudinal study (slide 48)
Understand the strengths and limitations of longitudinal studies (slide 48)
Recognize a cohort study (slide 45)
Understand the strengths and limitations of cohort studies (slide 46)
Recognize a randomized-controlled study (slide 47)
Understand the strengths and limitations of randomized-controlled studies (slide 47)
Recognize a before-after study (slide 48)
Understand the strengths and limitations of before-after studies (slide 48)
Recognize a crossover study (slide 48)
Understand the strengths and limitations of crossover studies (slide 48)
Recognize an open-label study (slide 49)
Understand the strengths and limitations of open-label studies (slide 49)
Recognize a post-hoc analysis (slide 49)
Understand the strengths and limitations of post-hoc analyses (slide 49)
Recognize a subgroup analysis (slide 49)
Understand the strengths and limitations of subgroup analyses (slide 49)
2. Bias and Confounding
Understand how bias affects the validity of results (slide 50)
Understand how confounding affects the validity of results (slide 50)
Identify common strategies in study design to avoid or reduce bias (slide 51)
Identify common strategies in study design to avoid or reduce confounding (slide 51)
Understand how study results may differ between distinct sub-populations (effect modification) (slide 52)

38. 3. Causation
Understand the difference between association and causation (slide 53)
Identify factors that strengthen causal inference in observational studies (eg, temporal sequence, dose response, repetition in a different population, consistency with other studies, biologic plausibility) (slide 54)
4. Incidence and Prevalence
Distinguish disease incidence from disease prevalence (slide 55)
5. Screening
Understand factors that affect the rationale for screening for a condition or disease (eg, prevalence, test accuracy, risk-benefit, disease burden, presence of a presymptomatic state) (slide 56)
6. Decision analysis
Understand the strengths and limitations of decision analyses (slide 57)
Interpret a decision analysis
7. Cost-benefit, cost-effectiveness, and outcomes
Differentiate cost-benefit from cost-effectiveness analysis (slide 58)
Understand how quality-adjusted life years are used in cost analyses (slide 58)
Understand the multiple perspectives (eg, of an individual, payor, society) that influence interpretation of cost-benefit and cost-effectiveness analyses (slide 58)
8. Sensitivity analysis
Understand the strengths and limitations of sensitivity analysis (slide 59)
Interpret the results of sensitivity analysis (slide 59)
9. Measurement
Understand the types of validity that relate to measurement (eg, face, construct, criterion, predictive, content) (slide 61)
Distinguish validity from reliability (slides 60, 61)
Distinguish internal from external validity (slide 61)
Distinguish accuracy from precision (slide 60)
Understand and interpret measurements of interobserver reliability (eg, kappa) (slide 60)
Understand and interpret Cronbach's alpha (slide 60)

39. Study Types Phases of Drug Studies
Description
Subjects I
Intended to find dose range that is tolerated and safe - MTD
Usually very small sample size, typically without a control group II
Preliminary efficacy information
Larger than Phase I – but still limited sample size
III
Definitive efficacy information and some common side effects
Usually large sample size – maybe blinded IV
Post-marketing surveillance – to detect rare side effects
Very large sample size
Phase III trials are not large enough to detect differences in the rate or even existence of uncommon side effects

40. Retrospective Study Exposures and outcomes have already occurred
Strengths:
Feasible and inexpensive
Limitations:
Limited availability of confounders
No control over when or how exposure or outcome measured
Recall bias

41. Case Series Describing similar cases, treatments or outcomes
Strengths:
Feasible and inexpensive
Limitations:
Cannot test hypotheses
Selection bias

42. Cross-Sectional Study
All measures obtained on a single occasion
Strengths:
Fast/inexpensive
No lost to follow-up
Limitations:
Difficult to establish causal relationships
Can measure prevalence – not incidence
For example, could survey all children in the province of Ontario and see whether there is a relationship between BMI and number of hours of television watching.

43. Case-Control Studies Identify those with and without outcome
Look BACK to see how many had potential predictor
CASES
Predictor
Present/Absent?
CONTROLS
For example, you could take 100 children with leukemia 100 age and sex matched health controls – and look to see how many of them had been hospitalized for asthma before the age of 2.

44. Case-Control Studies Strengths:
Good for rare outcomes or long latency between predictor and outcome
Limitations:
Cannot estimate incidence or prevalence of disease
Can only study one outcome
Prone to bias:
Sampling – selection of controls is critical
Recall bias
Strategies to overcome sampling bias:
Hospital or clinic based controls – use controls from the same facility in which cases were identified
Matching
Population-based sample
Use of two or more control groups

45. Cohort Studies Identify those with and without potential predictor
Look FORWARD to see how many have outcome
Can be prospective or retrospective
PRED YES
For example, you could identify children who were admitted to hospital to asthma and children hospitalized for another reason prior to two years of age. Then, could follow them forward for the next 16 years to see how many of them identified leukemia.
Outcome
Yes/No?
PRED NO

46. Cohort Studies Strengths: Limitations:
Time sequence strengthens inference
Absence of recall bias
Can calculate incidence
Limitations:
Expensive

47. Randomized Controlled Trials
Randomization:
Ensures that known and unknown potential confounders are equally distributed among the treatment and control groups
Avoid allocation bias
Strengths:
Strongest design to make inferences about therapy
Limits influence of confounders, allocation bias
Limitations:
Expensive
Usually lack generalizability
Allocation concealment should not be confused with blinding (see item 11). Allocation concealment seeks to prevent selection bias, protects the assignment sequence before and until allocation, and can always be successfully implemented (2). In contrast, blinding seeks to prevent performance* and ascertainment bias*, protects the sequence after allocation, and cannot always be implemented (21). Without adequate allocation concealment, however, even random, unpredictable assignment sequences can be subverted (2, 113).

48. Other Study Types
Longitudinal study: track same individuals over a period of time and repeat measurements
Strengths: natural history
Limitations: hard to determine causation, lost to follow-up
Before and after study: evaluate an outcome before and following institution of an intervention
Strengths: feasible
Limitations: confounders, regression to the mean
Crossover study: evaluate an outcome in two time periods within the same individual – typically randomize order
Strengths: reduces variability, improves power
Limitations: need chronic stable conditions, short onset of action, condition cannot be “cured” by intervention

49. Other Study Types Open label study: not blinded
Strengths: feasible, ethics
Limitations: co-interventions (may treat groups differently), contamination, observer bias
Post-hoc analysis: examining data after a study has been completed for relationships not hypothesized a priori
Limitations: multiple testing
Sub-group analysis: examining patterns in a sub-group of patients
Strengths: may be sub-groups of patients who respond differently to treatment
Limitations: multiple testing, limited power

50. Bias and Confounding Bias: systematic error
Selection bias
Measurement bias
Confounder: third variable that is associated with exposure and outcome variables and not in the causal pathway
Both bias and confounders are major threats to validity of any study

51. Strategies to Avoid Bias/Confounding
Bias: randomization, double-blinding, standardize measurement of outcomes
Confounding:
Restriction - only include specific sub-groups
Stratification - analyze by sub-group
Stratify randomization - ensure equal distribution
Multiple regression techniques

52. Effect Modifier Interaction Two exposure variables of interest
Effect of one exposure variable depends on the second exposure variable
Example: influence of BMI on SBP differs in males and females
Understand how results may differ between distinct sub-populations (effect modification)

53. Causation Association: correlation
Causation: outcome causally related to exposure

54. Factors that strengthen causal inference in observational studies (Bradford-Hill criteria):
Temporality – cause precedes effect
Strength - large relative risk
Dose-response
Reversibility
Consistency – repeatedly observed
Biologic plausibility
Specificity – one cause, one effect
Analogy – same relationship observed in a different disease

55. Incidence and Prevalence
Incidence: number of new cases per unit time
Prevalence: total number of cases

56. Screening Factors that affect rationale for screening:
Disease prevalence and burden (important)
Latent stage of disease (presymptomatic)
Test available, accurate and acceptable
Screening will do more benefit than harm
Availability of treatment

57. Decision Analysis
Decision analysis – quantitative method to determine the treatment option with the best expected outcome
Strengths:
Quantitative comparison of different treatment strategies that can incorporate benefits, side effects and costs
Limitations:
Less useful for individual patient decision making
Many probabilities and outcomes (such as quality of life) not known

58. Cost Analyses
Cost benefit analysis: both benefits and costs expressed in monetary terms - “net present value”
Cost effectiveness analysis: compares costs and outcomes of different strategies eg. cost per quality-adjusted life years (QALYs)
QALYs = quality of life * length of life
In cost effectiveness analysis, illustrates the cost to gain quality and quantity of life
Different perspective – individual, society, healthcare system – influence CBA and CEA

59. Sensitivity Analysis
Decision and cost analyses, probabilities and outcomes uncertain
Sensitivity analysis – vary probabilities and outcomes to determine if conclusions change
If findings don’t change, conclusions are robust

60. Measurement Reliability: consistency or reproducibility (precise)
Test re-test (repeated over time), interrater (repeated by different raters)
Measure agreement with kappa statistic (dichotomous outcomes)
Cronbach’s alpha-internal consistency
Extent to which items are correlated

61. Validity
Validity: degree to which it measures what it claims to measure (accurate)
Internal - within the specific study
External - generalizability
How to determine:
Face – do the items make sense
Content – does the instrument seem to contain the correct items
Criterion – if there is a gold standard
Construct – extent to which the measure behaves in the hypothesized manner
Predictive – extent to which a score predicts some criterion measure

62. Principles of Epidemiology and Clinical Research Design and Applying Research to Clinical Practice

63. C. Applying Research to Clinical Practice
1. Assessment of study design, performance & analysis (internal validity)
Recognize when appropriate control groups have been selected for a case-control study (slide 64)
Recognize when appropriate control groups have been selected for a cohort study (slide 64)
Recognize the use and limitations of surrogate endpoints (slide 64)
Understand the use of intent-to-treat analysis (slide 65)
Understand how sample size affects the power of a study (slide 65)
Understand how sample size may limit the ability to detect adverse events (slide 65)
Understand how to calculate an adequate sample size for a controlled trial (ie, clinically meaningful difference, variability in measurement, choice of alpha and beta) (slide 66)
2. Assessment of generalizability (external validity)
Identify factors that contribute to or jeopardize generalizability (slide 67)
Understand how non-representative samples can bias results (slide 67)
Assess how the data source (eg, diaries, billing data, discharge diagnostic code) may affect study results (slide 68)
3. Application of information for patient care
Estimate the post-test probability of a disease, given the pretest probability of the disease and the likelihood ratio for the test (slide 69)
Calculate absolute risk reduction (slide 69)
Calculate and interpret the number-needed-to treat (slide 69)
Distinguish statistical significance from clinical importance (slide 69)
4. Using the medical literature
Given the need for specific clinical information, identify a clear, structured, searchable clinical question (slide 70)
Identify the study design most likely to yield valid information about the accuracy of a diagnostic test (slide 70)
Identify the study design most likely to yield valid information about the benefits and/or harms of an intervention (slide 70)
Identify the study design most likely to yield valid information about the prognosis of a condition (slide 70)

64. Internal Validity Appropriate control groups for case-control study
From population at risk that are otherwise similar to cases
Appropriate control groups for cohort study
Non-exposed that are otherwise similar to exposed group
Surrogate endpoints:
Laboratory test or physical finding used instead of a clinically meaningful endpoint
Strengths: may be detected earlier or in more patients
Limitations: effects on surrogate endpoint may not correspond to effects on clinical endpoint

65. Intention to treat analysis:
Includes all randomized subjects to the group they were assigned
Important because patients do not cross-over randomly
Should be primary analysis
Power – probability test will reject the null hypothesis
Larger sample size
More power
Greater ability to detect adverse events

66. Sample Size Considerations
To calculate sample size for a controlled trial:
Alpha - typically 0.05 (5% probability of finding a difference by chance alone if one does not exist)
Beta - (1-power) - typically 0.1 or 0.2 (20% probability of not finding a difference if one really exists)
Minimal clinically important difference and variability of outcome

67. External Validity Factors that can jeopardize generalizability
Highly selected subjects
Intervention applied in a manner not feasible for routine practice
Maneuvers to enhance compliance
Excessive monitoring
If study sample not representative – biased because not generalizable

68. Influence of Data Sources
Diaries – missing data, recall bias
Administrative data – eg billings data, discharge diagnosis codes:
Validity needs to be established
Lack clinical information such as disease risk status
Errors

69. Application of Information
Post-test probability of disease = pretest probability of disease x likelihood ratio of test
ARR = risk (control) – risk (treatment) (slide 21)
NTT = 1/ARR
Significance
Statistical: set by alpha – more likely as sample size increases
Clinical: what is important – does not change with sample size

70. Using Medical Literature
To conduct search, need to identify a clear, structured searchable research question
Optimal study designs:
Purpose
Design That Yields Most Valid Information
Benefits and/or harms of an intervention
Randomized controlled trials
Prognosis
Cohort studies
Diagnostic test
Cross-sectional studies

71. Email: lillian.sung@sickkids.ca
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